Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations5004
Missing cells34413
Missing cells (%)26.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 MiB
Average record size in memory857.6 B

Variable types

Text3
Categorical15
Numeric5
Unsupported3

Alerts

cant_noAutenticado has constant value "1.0" Constant
cant_MontoLimite has constant value "1.0" Constant
Cluster_6 has constant value "6" Constant
Estado is highly overall correlated with periodo_preinscripcionHigh correlation
TipoSocietario is highly overall correlated with periodo_preinscripcionHigh correlation
anio_preinscripcion is highly overall correlated with antiguedad and 1 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with cant_representante and 2 other fieldsHigh correlation
cant_autenticado is highly overall correlated with cant_representante and 2 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with monto_total_adjudicadoHigh correlation
cant_representante is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
cant_socios is highly overall correlated with periodo_preinscripcionHigh correlation
dtotal_articulos_provee is highly overall correlated with periodo_preinscripcionHigh correlation
monto_total_adjudicado is highly overall correlated with cant_procesos_adjudicadoHigh correlation
periodo_preinscripcion is highly overall correlated with Estado and 10 other fieldsHigh correlation
provincia is highly overall correlated with periodo_preinscripcionHigh correlation
Estado is highly imbalanced (58.0%) Imbalance
TipoSocietario is highly imbalanced (54.5%) Imbalance
cant_Apoderado is highly imbalanced (96.7%) Imbalance
cant_representante is highly imbalanced (75.3%) Imbalance
cant_autenticado is highly imbalanced (94.3%) Imbalance
cant_sinMontoLimite is highly imbalanced (93.1%) Imbalance
Estado has 99 (2.0%) missing values Missing
TipoSocietario has 99 (2.0%) missing values Missing
antiguedad has 99 (2.0%) missing values Missing
cant_socios has 3577 (71.5%) missing values Missing
cant_apercibimientos has 5004 (100.0%) missing values Missing
cant_suspensiones has 5004 (100.0%) missing values Missing
cant_antecedentes has 5004 (100.0%) missing values Missing
cant_Apoderado has 222 (4.4%) missing values Missing
cant_representante has 4849 (96.9%) missing values Missing
cant_autenticado has 99 (2.0%) missing values Missing
cant_noAutenticado has 4988 (99.7%) missing values Missing
cant_sinMontoLimite has 99 (2.0%) missing values Missing
cant_MontoLimite has 5003 (> 99.9%) missing values Missing
total_articulos_provee has 99 (2.0%) missing values Missing
dtotal_articulos_provee has 99 (2.0%) missing values Missing
CUIT has unique values Unique
cant_apercibimientos is an unsupported type, check if it needs cleaning or further analysis Unsupported
cant_suspensiones is an unsupported type, check if it needs cleaning or further analysis Unsupported
cant_antecedentes is an unsupported type, check if it needs cleaning or further analysis Unsupported
periodo_preinscripcion has 99 (2.0%) zeros Zeros
antiguedad has 761 (15.2%) zeros Zeros

Reproduction

Analysis started2025-06-30 18:10:50.279952
Analysis finished2025-06-30 18:10:55.131052
Duration4.85 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct5004
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size371.2 KiB
2025-06-30T15:10:55.268000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length11
Mean length10.961831
Min length3

Characters and Unicode

Total characters54853
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5004 ?
Unique (%)100.0%

Sample

1st row27236909900
2nd row30569211685
3rd row30708415487
4th row30708538317
5th row20082883240
ValueCountFrequency (%)
33663419949 1
 
< 0.1%
27331126530 1
 
< 0.1%
27236909900 1
 
< 0.1%
30569211685 1
 
< 0.1%
30708415487 1
 
< 0.1%
30708538317 1
 
< 0.1%
20082883240 1
 
< 0.1%
30707882510 1
 
< 0.1%
20230506060 1
 
< 0.1%
20141208269 1
 
< 0.1%
Other values (4994) 4994
99.8%
2025-06-30T15:10:55.498162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 8786
16.0%
0 8084
14.7%
3 6409
11.7%
7 5715
10.4%
1 5562
10.1%
4 4165
7.6%
6 4137
7.5%
9 4111
7.5%
5 3931
7.2%
8 3880
7.1%
Other values (19) 73
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54853
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 8786
16.0%
0 8084
14.7%
3 6409
11.7%
7 5715
10.4%
1 5562
10.1%
4 4165
7.6%
6 4137
7.5%
9 4111
7.5%
5 3931
7.2%
8 3880
7.1%
Other values (19) 73
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54853
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 8786
16.0%
0 8084
14.7%
3 6409
11.7%
7 5715
10.4%
1 5562
10.1%
4 4165
7.6%
6 4137
7.5%
9 4111
7.5%
5 3931
7.2%
8 3880
7.1%
Other values (19) 73
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54853
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 8786
16.0%
0 8084
14.7%
3 6409
11.7%
7 5715
10.4%
1 5562
10.1%
4 4165
7.6%
6 4137
7.5%
9 4111
7.5%
5 3931
7.2%
8 3880
7.1%
Other values (19) 73
 
0.1%

Nombre
Text

Distinct4019
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Memory size413.4 KiB
2025-06-30T15:10:55.661429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length99
Median length65
Mean length16.866507
Min length1

Characters and Unicode

Total characters84400
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4015 ?
Unique (%)80.2%

Sample

1st rowEMR VENTAS & SERVICIOS
2nd rowElectricidad Chiclana de R. Santoianni y O.S. Rodriguez
3rd rowYLUM S.A.
4th rowCAROLS SA
5th rowSEGUMAX de HORACIO MIGUEL ESPOSITO
ValueCountFrequency (%)
sin 984
 
7.5%
datos 983
 
7.5%
s.a 391
 
3.0%
srl 373
 
2.9%
de 257
 
2.0%
sa 207
 
1.6%
s.r.l 183
 
1.4%
y 164
 
1.3%
servicios 145
 
1.1%
del 89
 
0.7%
Other values (5012) 9285
71.1%
2025-06-30T15:10:55.920086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8058
 
9.5%
A 5861
 
6.9%
E 3977
 
4.7%
S 3945
 
4.7%
I 3841
 
4.6%
R 3818
 
4.5%
a 3714
 
4.4%
s 3374
 
4.0%
i 3215
 
3.8%
O 3193
 
3.8%
Other values (86) 41404
49.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8058
 
9.5%
A 5861
 
6.9%
E 3977
 
4.7%
S 3945
 
4.7%
I 3841
 
4.6%
R 3818
 
4.5%
a 3714
 
4.4%
s 3374
 
4.0%
i 3215
 
3.8%
O 3193
 
3.8%
Other values (86) 41404
49.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8058
 
9.5%
A 5861
 
6.9%
E 3977
 
4.7%
S 3945
 
4.7%
I 3841
 
4.6%
R 3818
 
4.5%
a 3714
 
4.4%
s 3374
 
4.0%
i 3215
 
3.8%
O 3193
 
3.8%
Other values (86) 41404
49.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8058
 
9.5%
A 5861
 
6.9%
E 3977
 
4.7%
S 3945
 
4.7%
I 3841
 
4.6%
R 3818
 
4.5%
a 3714
 
4.4%
s 3374
 
4.0%
i 3215
 
3.8%
O 3193
 
3.8%
Other values (86) 41404
49.1%
Distinct1569
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Memory size366.4 KiB
2025-06-30T15:10:56.087102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9802158
Min length9

Characters and Unicode

Total characters49941
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique536 ?
Unique (%)10.7%

Sample

1st row04/10/2016
2nd row18/08/2016
3rd row24/08/2016
4th row09/09/2016
5th row18/10/2016
ValueCountFrequency (%)
datos 99
 
1.9%
sin 99
 
1.9%
17/11/2021 26
 
0.5%
23/11/2016 17
 
0.3%
27/12/2016 16
 
0.3%
06/12/2016 16
 
0.3%
29/03/2017 15
 
0.3%
17/11/2016 15
 
0.3%
27/10/2016 14
 
0.3%
09/08/2017 14
 
0.3%
Other values (1560) 4772
93.5%
2025-06-30T15:10:56.322420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11247
22.5%
/ 9810
19.6%
2 9200
18.4%
1 8460
16.9%
7 2549
 
5.1%
8 1811
 
3.6%
6 1519
 
3.0%
9 1451
 
2.9%
3 1196
 
2.4%
5 939
 
1.9%
Other values (9) 1759
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 11247
22.5%
/ 9810
19.6%
2 9200
18.4%
1 8460
16.9%
7 2549
 
5.1%
8 1811
 
3.6%
6 1519
 
3.0%
9 1451
 
2.9%
3 1196
 
2.4%
5 939
 
1.9%
Other values (9) 1759
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 11247
22.5%
/ 9810
19.6%
2 9200
18.4%
1 8460
16.9%
7 2549
 
5.1%
8 1811
 
3.6%
6 1519
 
3.0%
9 1451
 
2.9%
3 1196
 
2.4%
5 939
 
1.9%
Other values (9) 1759
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 11247
22.5%
/ 9810
19.6%
2 9200
18.4%
1 8460
16.9%
7 2549
 
5.1%
8 1811
 
3.6%
6 1519
 
3.0%
9 1451
 
2.9%
3 1196
 
2.4%
5 939
 
1.9%
Other values (9) 1759
 
3.5%

Estado
Categorical

High correlation  Imbalance  Missing 

Distinct7
Distinct (%)0.1%
Missing99
Missing (%)2.0%
Memory size375.6 KiB
Inscripto
3794 
Pre Inscripto
521 
Desactualizado doc. vencidos
399 
Desactualizado mantención
 
61
Con Solicitud De Baja
 
58
Other values (2)
 
72

Length

Max length28
Median length9
Mean length11.495617
Min length9

Characters and Unicode

Total characters56386
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInscripto
2nd rowDesactualizado doc. vencidos
3rd rowInscripto
4th rowInscripto
5th rowDesactualizado doc. vencidos

Common Values

ValueCountFrequency (%)
Inscripto 3794
75.8%
Pre Inscripto 521
 
10.4%
Desactualizado doc. vencidos 399
 
8.0%
Desactualizado mantención 61
 
1.2%
Con Solicitud De Baja 58
 
1.2%
Desactualizado Por Clase 56
 
1.1%
En Evaluacion 16
 
0.3%
(Missing) 99
 
2.0%

Length

2025-06-30T15:10:56.417043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:56.527828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 4315
65.5%
pre 521
 
7.9%
desactualizado 516
 
7.8%
doc 399
 
6.1%
vencidos 399
 
6.1%
mantención 61
 
0.9%
con 58
 
0.9%
solicitud 58
 
0.9%
de 58
 
0.9%
baja 58
 
0.9%
Other values (4) 144
 
2.2%

Most occurring characters

ValueCountFrequency (%)
o 5817
10.3%
c 5764
10.2%
i 5423
9.6%
s 5286
9.4%
n 4987
8.8%
t 4950
8.8%
r 4892
8.7%
I 4315
7.7%
p 4315
7.7%
a 1813
 
3.2%
Other values (17) 8824
15.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56386
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5817
10.3%
c 5764
10.2%
i 5423
9.6%
s 5286
9.4%
n 4987
8.8%
t 4950
8.8%
r 4892
8.7%
I 4315
7.7%
p 4315
7.7%
a 1813
 
3.2%
Other values (17) 8824
15.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56386
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5817
10.3%
c 5764
10.2%
i 5423
9.6%
s 5286
9.4%
n 4987
8.8%
t 4950
8.8%
r 4892
8.7%
I 4315
7.7%
p 4315
7.7%
a 1813
 
3.2%
Other values (17) 8824
15.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56386
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5817
10.3%
c 5764
10.2%
i 5423
9.6%
s 5286
9.4%
n 4987
8.8%
t 4950
8.8%
r 4892
8.7%
I 4315
7.7%
p 4315
7.7%
a 1813
 
3.2%
Other values (17) 8824
15.6%

TipoSocietario
Categorical

High correlation  Imbalance  Missing 

Distinct10
Distinct (%)0.2%
Missing99
Missing (%)2.0%
Memory size541.5 KiB
Persona Física
3390 
Sociedad Anónima
633 
S.R.L
578 
Otras Formas Societarias
 
89
PJ Extranjero Sin Sucursal
 
68
Other values (5)
 
147

Length

Max length29
Median length14
Mean length13.669317
Min length5

Characters and Unicode

Total characters67048
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowPersona Física
2nd rowSociedades De Hecho
3rd rowSociedad Anónima
4th rowSociedad Anónima
5th rowPersona Física

Common Values

ValueCountFrequency (%)
Persona Física 3390
67.7%
Sociedad Anónima 633
 
12.6%
S.R.L 578
 
11.6%
Otras Formas Societarias 89
 
1.8%
PJ Extranjero Sin Sucursal 68
 
1.4%
Organismo Publico 65
 
1.3%
Sociedades De Hecho 34
 
0.7%
Cooperativas 24
 
0.5%
PF Extranjero No Residente 23
 
0.5%
Unión Transitoria de Empresas 1
 
< 0.1%
(Missing) 99
 
2.0%

Length

2025-06-30T15:10:56.662443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:56.740527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
persona 3390
35.6%
física 3390
35.6%
sociedad 633
 
6.7%
anónima 633
 
6.7%
s.r.l 578
 
6.1%
extranjero 91
 
1.0%
formas 89
 
0.9%
otras 89
 
0.9%
societarias 89
 
0.9%
pj 68
 
0.7%
Other values (14) 465
 
4.9%

Most occurring characters

ValueCountFrequency (%)
a 8711
13.0%
s 7264
10.8%
i 5116
 
7.6%
n 4906
 
7.3%
4610
 
6.9%
o 4562
 
6.8%
e 4434
 
6.6%
c 4313
 
6.4%
r 3999
 
6.0%
P 3546
 
5.3%
Other values (29) 15587
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8711
13.0%
s 7264
10.8%
i 5116
 
7.6%
n 4906
 
7.3%
4610
 
6.9%
o 4562
 
6.8%
e 4434
 
6.6%
c 4313
 
6.4%
r 3999
 
6.0%
P 3546
 
5.3%
Other values (29) 15587
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8711
13.0%
s 7264
10.8%
i 5116
 
7.6%
n 4906
 
7.3%
4610
 
6.9%
o 4562
 
6.8%
e 4434
 
6.6%
c 4313
 
6.4%
r 3999
 
6.0%
P 3546
 
5.3%
Other values (29) 15587
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8711
13.0%
s 7264
10.8%
i 5116
 
7.6%
n 4906
 
7.3%
4610
 
6.9%
o 4562
 
6.8%
e 4434
 
6.6%
c 4313
 
6.4%
r 3999
 
6.0%
P 3546
 
5.3%
Other values (29) 15587
23.2%

periodo_preinscripcion
Real number (ℝ)

High correlation  Zeros 

Distinct80
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197839.84
Minimum0
Maximum202303
Zeros99
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-06-30T15:10:56.860021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile201610
Q1201704
median201803
Q3201911
95-th percentile202202
Maximum202303
Range202303
Interquartile range (IQR)207

Descriptive statistics

Standard deviation28110.157
Coefficient of variation (CV)0.14208542
Kurtosis45.608806
Mean197839.84
Median Absolute Deviation (MAD)101
Skewness-6.8984538
Sum9.8999056 × 108
Variance7.9018095 × 108
MonotonicityNot monotonic
2025-06-30T15:10:56.985005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201611 204
 
4.1%
201706 170
 
3.4%
201705 167
 
3.3%
201612 166
 
3.3%
201701 163
 
3.3%
201703 159
 
3.2%
201704 149
 
3.0%
201708 144
 
2.9%
201707 143
 
2.9%
201702 134
 
2.7%
Other values (70) 3405
68.0%
ValueCountFrequency (%)
0 99
2.0%
201607 17
 
0.3%
201608 49
 
1.0%
201609 42
 
0.8%
201610 89
1.8%
201611 204
4.1%
201612 166
3.3%
201701 163
3.3%
201702 134
2.7%
201703 159
3.2%
ValueCountFrequency (%)
202303 1
 
< 0.1%
202212 1
 
< 0.1%
202211 6
 
0.1%
202210 10
 
0.2%
202209 32
0.6%
202208 38
0.8%
202207 19
0.4%
202206 28
0.6%
202205 35
0.7%
202204 34
0.7%

anio_preinscripcion
Categorical

High correlation 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
2017
1619 
2018
925 
2019
581 
2016
567 
2021
483 
Other values (4)
829 

Length

Max length9
Median length4
Mean length4.0989209
Min length4

Characters and Unicode

Total characters20511
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2017 1619
32.4%
2018 925
18.5%
2019 581
 
11.6%
2016 567
 
11.3%
2021 483
 
9.7%
2020 452
 
9.0%
2022 277
 
5.5%
sin datos 99
 
2.0%
2023 1
 
< 0.1%

Length

2025-06-30T15:10:57.078669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:57.156863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2017 1619
31.7%
2018 925
18.1%
2019 581
 
11.4%
2016 567
 
11.1%
2021 483
 
9.5%
2020 452
 
8.9%
2022 277
 
5.4%
sin 99
 
1.9%
datos 99
 
1.9%
2023 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 6395
31.2%
0 5357
26.1%
1 4175
20.4%
7 1619
 
7.9%
8 925
 
4.5%
9 581
 
2.8%
6 567
 
2.8%
s 198
 
1.0%
i 99
 
0.5%
n 99
 
0.5%
Other values (6) 496
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20511
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 6395
31.2%
0 5357
26.1%
1 4175
20.4%
7 1619
 
7.9%
8 925
 
4.5%
9 581
 
2.8%
6 567
 
2.8%
s 198
 
1.0%
i 99
 
0.5%
n 99
 
0.5%
Other values (6) 496
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20511
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 6395
31.2%
0 5357
26.1%
1 4175
20.4%
7 1619
 
7.9%
8 925
 
4.5%
9 581
 
2.8%
6 567
 
2.8%
s 198
 
1.0%
i 99
 
0.5%
n 99
 
0.5%
Other values (6) 496
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20511
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 6395
31.2%
0 5357
26.1%
1 4175
20.4%
7 1619
 
7.9%
8 925
 
4.5%
9 581
 
2.8%
6 567
 
2.8%
s 198
 
1.0%
i 99
 
0.5%
n 99
 
0.5%
Other values (6) 496
 
2.4%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct163
Distinct (%)3.3%
Missing23
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean9.6217627
Minimum1
Maximum613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-06-30T15:10:57.268880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile36
Maximum613
Range612
Interquartile range (IQR)4

Descriptive statistics

Standard deviation35.442247
Coefficient of variation (CV)3.6835503
Kurtosis101.00798
Mean9.6217627
Median Absolute Deviation (MAD)1
Skewness8.9673997
Sum47926
Variance1256.1529
MonotonicityNot monotonic
2025-06-30T15:10:57.380718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2103
42.0%
2 884
17.7%
3 445
 
8.9%
4 281
 
5.6%
5 166
 
3.3%
6 136
 
2.7%
7 108
 
2.2%
8 71
 
1.4%
9 61
 
1.2%
11 56
 
1.1%
Other values (153) 670
 
13.4%
ValueCountFrequency (%)
1 2103
42.0%
2 884
17.7%
3 445
 
8.9%
4 281
 
5.6%
5 166
 
3.3%
6 136
 
2.7%
7 108
 
2.2%
8 71
 
1.4%
9 61
 
1.2%
10 51
 
1.0%
ValueCountFrequency (%)
613 1
< 0.1%
590 1
< 0.1%
551 1
< 0.1%
525 1
< 0.1%
468 1
< 0.1%
455 1
< 0.1%
438 1
< 0.1%
417 1
< 0.1%
389 1
< 0.1%
384 1
< 0.1%

monto_total_adjudicado
Real number (ℝ)

High correlation 

Distinct4806
Distinct (%)96.5%
Missing23
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean22399144
Minimum0
Maximum1.520047 × 109
Zeros42
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-06-30T15:10:57.474988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29502
Q1406454.55
median2001829.6
Q39635828.5
95-th percentile92366985
Maximum1.520047 × 109
Range1.520047 × 109
Interquartile range (IQR)9229374

Descriptive statistics

Standard deviation87195178
Coefficient of variation (CV)3.8927906
Kurtosis89.26129
Mean22399144
Median Absolute Deviation (MAD)1895103.4
Skewness8.3896137
Sum1.1157014 × 1011
Variance7.602999 × 1015
MonotonicityNot monotonic
2025-06-30T15:10:57.765290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
 
0.8%
6710526.316 7
 
0.1%
240428.5714 4
 
0.1%
255000 4
 
0.1%
40800 4
 
0.1%
526451.6129 4
 
0.1%
114750 4
 
0.1%
85000 4
 
0.1%
21857.14286 4
 
0.1%
459000 3
 
0.1%
Other values (4796) 4901
97.9%
(Missing) 23
 
0.5%
ValueCountFrequency (%)
0 42
0.8%
0.010851064 1
 
< 0.1%
0.056666667 1
 
< 0.1%
0.80952381 1
 
< 0.1%
1.65952381 1
 
< 0.1%
2.487804878 2
 
< 0.1%
12.648 1
 
< 0.1%
120.2142857 1
 
< 0.1%
141.24 1
 
< 0.1%
154.1027027 1
 
< 0.1%
ValueCountFrequency (%)
1520047045 1
< 0.1%
1501157408 1
< 0.1%
1155438067 1
< 0.1%
1061872013 1
< 0.1%
1052539603 1
< 0.1%
1023647402 1
< 0.1%
1006323598 1
< 0.1%
1004124327 1
< 0.1%
1001211883 1
< 0.1%
998054641.4 1
< 0.1%

antiguedad
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct6
Distinct (%)0.1%
Missing99
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean2.7930683
Minimum0
Maximum5
Zeros761
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-06-30T15:10:57.843410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6228238
Coefficient of variation (CV)0.58101831
Kurtosis-0.99706376
Mean2.7930683
Median Absolute Deviation (MAD)1
Skewness-0.50252539
Sum13700
Variance2.6335572
MonotonicityNot monotonic
2025-06-30T15:10:57.921525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 1619
32.4%
3 925
18.5%
0 761
15.2%
2 581
 
11.6%
5 567
 
11.3%
1 452
 
9.0%
(Missing) 99
 
2.0%
ValueCountFrequency (%)
0 761
15.2%
1 452
 
9.0%
2 581
 
11.6%
3 925
18.5%
4 1619
32.4%
5 567
 
11.3%
ValueCountFrequency (%)
5 567
 
11.3%
4 1619
32.4%
3 925
18.5%
2 581
 
11.6%
1 452
 
9.0%
0 761
15.2%

provincia
Categorical

High correlation 

Distinct28
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size371.3 KiB
CABA
1474 
Buenos Aires
1288 
Córdoba
317 
Santa Fe
175 
Mendoza
 
153
Other values (23)
1597 

Length

Max length19
Median length16
Mean length7.9426459
Min length1

Characters and Unicode

Total characters39745
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowChubut
2nd rowCABA
3rd rowCABA
4th rowCABA
5th rowCABA

Common Values

ValueCountFrequency (%)
CABA 1474
29.5%
Buenos Aires 1288
25.7%
Córdoba 317
 
6.3%
Santa Fe 175
 
3.5%
Mendoza 153
 
3.1%
Chubut 138
 
2.8%
sin datos 122
 
2.4%
Misiones 118
 
2.4%
Rio Negro 116
 
2.3%
Entre Rios 96
 
1.9%
Other values (18) 1007
20.1%

Length

2025-06-30T15:10:57.999559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
caba 1474
20.2%
buenos 1288
17.6%
aires 1288
17.6%
córdoba 317
 
4.3%
santa 254
 
3.5%
fe 175
 
2.4%
mendoza 153
 
2.1%
chubut 138
 
1.9%
del 123
 
1.7%
datos 122
 
1.7%
Other values (27) 1977
27.0%

Most occurring characters

ValueCountFrequency (%)
A 4236
 
10.7%
e 3866
 
9.7%
s 3380
 
8.5%
o 2834
 
7.1%
B 2762
 
6.9%
n 2529
 
6.4%
r 2509
 
6.3%
2305
 
5.8%
a 2275
 
5.7%
C 2201
 
5.5%
Other values (31) 10848
27.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 39745
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 4236
 
10.7%
e 3866
 
9.7%
s 3380
 
8.5%
o 2834
 
7.1%
B 2762
 
6.9%
n 2529
 
6.4%
r 2509
 
6.3%
2305
 
5.8%
a 2275
 
5.7%
C 2201
 
5.5%
Other values (31) 10848
27.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 39745
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 4236
 
10.7%
e 3866
 
9.7%
s 3380
 
8.5%
o 2834
 
7.1%
B 2762
 
6.9%
n 2529
 
6.4%
r 2509
 
6.3%
2305
 
5.8%
a 2275
 
5.7%
C 2201
 
5.5%
Other values (31) 10848
27.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 39745
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 4236
 
10.7%
e 3866
 
9.7%
s 3380
 
8.5%
o 2834
 
7.1%
B 2762
 
6.9%
n 2529
 
6.4%
r 2509
 
6.3%
2305
 
5.8%
a 2275
 
5.7%
C 2201
 
5.5%
Other values (31) 10848
27.3%

cant_socios
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.4%
Missing3577
Missing (%)71.5%
Memory size318.3 KiB
1.0
723 
2.0
619 
3.0
 
64
4.0
 
18
5.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4281
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row4.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 723
 
14.4%
2.0 619
 
12.4%
3.0 64
 
1.3%
4.0 18
 
0.4%
5.0 3
 
0.1%
(Missing) 3577
71.5%

Length

2025-06-30T15:10:58.093299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:58.156379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 723
50.7%
2.0 619
43.4%
3.0 64
 
4.5%
4.0 18
 
1.3%
5.0 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
. 1427
33.3%
0 1427
33.3%
1 723
16.9%
2 619
14.5%
3 64
 
1.5%
4 18
 
0.4%
5 3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1427
33.3%
0 1427
33.3%
1 723
16.9%
2 619
14.5%
3 64
 
1.5%
4 18
 
0.4%
5 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1427
33.3%
0 1427
33.3%
1 723
16.9%
2 619
14.5%
3 64
 
1.5%
4 18
 
0.4%
5 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1427
33.3%
0 1427
33.3%
1 723
16.9%
2 619
14.5%
3 64
 
1.5%
4 18
 
0.4%
5 3
 
0.1%

cant_apercibimientos
Unsupported

Missing  Rejected  Unsupported 

Missing5004
Missing (%)100.0%
Memory size78.2 KiB

cant_suspensiones
Unsupported

Missing  Rejected  Unsupported 

Missing5004
Missing (%)100.0%
Memory size78.2 KiB

cant_antecedentes
Unsupported

Missing  Rejected  Unsupported 

Missing5004
Missing (%)100.0%
Memory size78.2 KiB

cant_Apoderado
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.1%
Missing222
Missing (%)4.4%
Memory size331.4 KiB
1.0
4756 
2.0
 
23
3.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14346
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4756
95.0%
2.0 23
 
0.5%
3.0 3
 
0.1%
(Missing) 222
 
4.4%

Length

2025-06-30T15:10:58.228372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:58.275254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4756
99.5%
2.0 23
 
0.5%
3.0 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 4782
33.3%
0 4782
33.3%
1 4756
33.2%
2 23
 
0.2%
3 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4782
33.3%
0 4782
33.3%
1 4756
33.2%
2 23
 
0.2%
3 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4782
33.3%
0 4782
33.3%
1 4756
33.2%
2 23
 
0.2%
3 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4782
33.3%
0 4782
33.3%
1 4756
33.2%
2 23
 
0.2%
3 3
 
< 0.1%

cant_representante
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)1.9%
Missing4849
Missing (%)96.9%
Memory size313.4 KiB
1.0
145 
2.0
 
8
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters465
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 145
 
2.9%
2.0 8
 
0.2%
3.0 2
 
< 0.1%
(Missing) 4849
96.9%

Length

2025-06-30T15:10:58.337748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:58.385311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 145
93.5%
2.0 8
 
5.2%
3.0 2
 
1.3%

Most occurring characters

ValueCountFrequency (%)
. 155
33.3%
0 155
33.3%
1 145
31.2%
2 8
 
1.7%
3 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 465
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 155
33.3%
0 155
33.3%
1 145
31.2%
2 8
 
1.7%
3 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 465
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 155
33.3%
0 155
33.3%
1 145
31.2%
2 8
 
1.7%
3 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 465
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 155
33.3%
0 155
33.3%
1 145
31.2%
2 8
 
1.7%
3 2
 
0.4%

cant_autenticado
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.1%
Missing99
Missing (%)2.0%
Memory size331.9 KiB
1.0
4851 
2.0
 
51
3.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14715
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4851
96.9%
2.0 51
 
1.0%
3.0 3
 
0.1%
(Missing) 99
 
2.0%

Length

2025-06-30T15:10:58.447803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:58.505755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4851
98.9%
2.0 51
 
1.0%
3.0 3
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 4905
33.3%
0 4905
33.3%
1 4851
33.0%
2 51
 
0.3%
3 3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14715
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4905
33.3%
0 4905
33.3%
1 4851
33.0%
2 51
 
0.3%
3 3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14715
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4905
33.3%
0 4905
33.3%
1 4851
33.0%
2 51
 
0.3%
3 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14715
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4905
33.3%
0 4905
33.3%
1 4851
33.0%
2 51
 
0.3%
3 3
 
< 0.1%

cant_noAutenticado
Categorical

Constant  Missing 

Distinct1
Distinct (%)6.2%
Missing4988
Missing (%)99.7%
Memory size312.8 KiB
1.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters48
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 16
 
0.3%
(Missing) 4988
99.7%

Length

2025-06-30T15:10:58.559357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:58.606224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 16
100.0%

Most occurring characters

ValueCountFrequency (%)
1 16
33.3%
. 16
33.3%
0 16
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 16
33.3%
. 16
33.3%
0 16
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 16
33.3%
. 16
33.3%
0 16
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 48
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 16
33.3%
. 16
33.3%
0 16
33.3%

cant_sinMontoLimite
Categorical

High correlation  Imbalance  Missing 

Distinct3
Distinct (%)0.1%
Missing99
Missing (%)2.0%
Memory size331.9 KiB
1.0
4839 
2.0
 
59
3.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters14715
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4839
96.7%
2.0 59
 
1.2%
3.0 7
 
0.1%
(Missing) 99
 
2.0%

Length

2025-06-30T15:10:58.668720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:58.715584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4839
98.7%
2.0 59
 
1.2%
3.0 7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 4905
33.3%
0 4905
33.3%
1 4839
32.9%
2 59
 
0.4%
3 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14715
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 4905
33.3%
0 4905
33.3%
1 4839
32.9%
2 59
 
0.4%
3 7
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14715
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 4905
33.3%
0 4905
33.3%
1 4839
32.9%
2 59
 
0.4%
3 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14715
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 4905
33.3%
0 4905
33.3%
1 4839
32.9%
2 59
 
0.4%
3 7
 
< 0.1%

cant_MontoLimite
Categorical

Constant  Missing 

Distinct1
Distinct (%)100.0%
Missing5003
Missing (%)> 99.9%
Memory size312.8 KiB
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row1.0

Common Values

ValueCountFrequency (%)
1.0 1
 
< 0.1%
(Missing) 5003
> 99.9%

Length

2025-06-30T15:10:58.778041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:58.824953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1
33.3%
. 1
33.3%
0 1
33.3%

total_articulos_provee
Real number (ℝ)

Missing 

Distinct610
Distinct (%)12.4%
Missing99
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean86.438124
Minimum1
Maximum2868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-06-30T15:10:58.887447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q346
95-th percentile541.6
Maximum2868
Range2867
Interquartile range (IQR)45

Descriptive statistics

Standard deviation213.0623
Coefficient of variation (CV)2.4649111
Kurtosis26.533974
Mean86.438124
Median Absolute Deviation (MAD)6
Skewness4.3477348
Sum423979
Variance45395.542
MonotonicityNot monotonic
2025-06-30T15:10:58.996803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1413
28.2%
2 350
 
7.0%
3 229
 
4.6%
5 162
 
3.2%
4 156
 
3.1%
6 138
 
2.8%
8 88
 
1.8%
7 86
 
1.7%
9 77
 
1.5%
10 68
 
1.4%
Other values (600) 2138
42.7%
(Missing) 99
 
2.0%
ValueCountFrequency (%)
1 1413
28.2%
2 350
 
7.0%
3 229
 
4.6%
4 156
 
3.1%
5 162
 
3.2%
6 138
 
2.8%
7 86
 
1.7%
8 88
 
1.8%
9 77
 
1.5%
10 68
 
1.4%
ValueCountFrequency (%)
2868 1
< 0.1%
2339 1
< 0.1%
2330 1
< 0.1%
2189 1
< 0.1%
1935 1
< 0.1%
1742 1
< 0.1%
1732 1
< 0.1%
1715 1
< 0.1%
1699 1
< 0.1%
1681 1
< 0.1%
Distinct21
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size418.0 KiB
(377.939- 599.760]
 
319
(33.011- 104.767]
 
316
(104.767- 224.078]
 
315
(1.302.657- 1.793.326]
 
308
(224.078- 377.939]
 
306
Other values (16)
3440 

Length

Max length29
Median length25
Mean length20.546163
Min length3

Characters and Unicode

Total characters102813
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(9.424.898- 13.557.176]
2nd row(4.727.330- 6.702.697]
3rd row(46.718.747- 89.439.449]
4th row(46.718.747- 89.439.449]
5th row(890.758- 1.302.657]

Common Values

ValueCountFrequency (%)
(377.939- 599.760] 319
 
6.4%
(33.011- 104.767] 316
 
6.3%
(104.767- 224.078] 315
 
6.3%
(1.302.657- 1.793.326] 308
 
6.2%
(224.078- 377.939] 306
 
6.1%
(890.758- 1.302.657] 303
 
6.1%
(599.760- 890.758] 284
 
5.7%
(1.793.326- 2.483.085] 282
 
5.6%
(2.483.085- 3.396.600] 274
 
5.5%
(-0- 33.011] 272
 
5.4%
Other values (11) 2025
40.5%

Length

2025-06-30T15:10:59.098953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
104.767 631
 
6.3%
377.939 625
 
6.3%
224.078 621
 
6.2%
1.302.657 611
 
6.1%
599.760 603
 
6.0%
1.793.326 590
 
5.9%
33.011 588
 
5.9%
890.758 587
 
5.9%
2.483.085 556
 
5.6%
3.396.600 529
 
5.3%
Other values (12) 4044
40.5%

Most occurring characters

ValueCountFrequency (%)
. 15830
15.4%
7 10242
10.0%
3 8402
 
8.2%
9 8317
 
8.1%
0 7020
 
6.8%
6 6021
 
5.9%
1 5810
 
5.7%
2 5765
 
5.6%
4 5736
 
5.6%
- 5253
 
5.1%
Other values (7) 24417
23.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 102813
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 15830
15.4%
7 10242
10.0%
3 8402
 
8.2%
9 8317
 
8.1%
0 7020
 
6.8%
6 6021
 
5.9%
1 5810
 
5.7%
2 5765
 
5.6%
4 5736
 
5.6%
- 5253
 
5.1%
Other values (7) 24417
23.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 102813
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 15830
15.4%
7 10242
10.0%
3 8402
 
8.2%
9 8317
 
8.1%
0 7020
 
6.8%
6 6021
 
5.9%
1 5810
 
5.7%
2 5765
 
5.6%
4 5736
 
5.6%
- 5253
 
5.1%
Other values (7) 24417
23.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 102813
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 15830
15.4%
7 10242
10.0%
3 8402
 
8.2%
9 8317
 
8.1%
0 7020
 
6.8%
6 6021
 
5.9%
1 5810
 
5.7%
2 5765
 
5.6%
4 5736
 
5.6%
- 5253
 
5.1%
Other values (7) 24417
23.7%
Distinct10
Distinct (%)0.2%
Missing23
Missing (%)0.5%
Memory size373.9 KiB
(0.999, 2.0]
2987 
(2.0, 3.0]
445 
(3.0, 4.0]
 
281
(39.0, 1214.0]
 
223
(8.0, 12.0]
 
202
Other values (5)
843 

Length

Max length14
Median length12
Mean length11.564345
Min length10

Characters and Unicode

Total characters57602
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(39.0, 1214.0]
2nd row(6.0, 8.0]
3rd row(39.0, 1214.0]
4th row(19.0, 39.0]
5th row(0.999, 2.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 2987
59.7%
(2.0, 3.0] 445
 
8.9%
(3.0, 4.0] 281
 
5.6%
(39.0, 1214.0] 223
 
4.5%
(8.0, 12.0] 202
 
4.0%
(19.0, 39.0] 186
 
3.7%
(6.0, 8.0] 179
 
3.6%
(12.0, 19.0] 176
 
3.5%
(4.0, 5.0] 166
 
3.3%
(5.0, 6.0] 136
 
2.7%
(Missing) 23
 
0.5%

Length

2025-06-30T15:10:59.192692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:59.271493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 3432
34.5%
0.999 2987
30.0%
3.0 726
 
7.3%
4.0 447
 
4.5%
39.0 409
 
4.1%
8.0 381
 
3.8%
12.0 378
 
3.8%
19.0 362
 
3.6%
6.0 315
 
3.2%
5.0 302
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 9962
17.3%
. 9962
17.3%
9 9732
16.9%
( 4981
8.6%
, 4981
8.6%
4981
8.6%
] 4981
8.6%
2 4033
7.0%
1 1186
 
2.1%
3 1135
 
2.0%
Other values (4) 1668
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57602
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9962
17.3%
. 9962
17.3%
9 9732
16.9%
( 4981
8.6%
, 4981
8.6%
4981
8.6%
] 4981
8.6%
2 4033
7.0%
1 1186
 
2.1%
3 1135
 
2.0%
Other values (4) 1668
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57602
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9962
17.3%
. 9962
17.3%
9 9732
16.9%
( 4981
8.6%
, 4981
8.6%
4981
8.6%
] 4981
8.6%
2 4033
7.0%
1 1186
 
2.1%
3 1135
 
2.0%
Other values (4) 1668
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57602
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9962
17.3%
. 9962
17.3%
9 9732
16.9%
( 4981
8.6%
, 4981
8.6%
4981
8.6%
] 4981
8.6%
2 4033
7.0%
1 1186
 
2.1%
3 1135
 
2.0%
Other values (4) 1668
 
2.9%

dtotal_articulos_provee
Categorical

High correlation  Missing 

Distinct15
Distinct (%)0.3%
Missing99
Missing (%)2.0%
Memory size375.1 KiB
(0.999, 2.0]
1763 
(345.0, 6993.0]
400 
(4.0, 6.0]
300 
(161.0, 345.0]
274 
(2.0, 3.0]
229 
Other values (10)
1939 

Length

Max length15
Median length12
Mean length12.009378
Min length10

Characters and Unicode

Total characters58906
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(58.0, 97.6]
2nd row(161.0, 345.0]
3rd row(58.0, 97.6]
4th row(58.0, 97.6]
5th row(161.0, 345.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 1763
35.2%
(345.0, 6993.0] 400
 
8.0%
(4.0, 6.0] 300
 
6.0%
(161.0, 345.0] 274
 
5.5%
(2.0, 3.0] 229
 
4.6%
(97.6, 161.0] 229
 
4.6%
(8.0, 11.0] 213
 
4.3%
(40.0, 58.0] 208
 
4.2%
(58.0, 97.6] 200
 
4.0%
(15.0, 21.0] 195
 
3.9%
Other values (5) 894
17.9%

Length

2025-06-30T15:10:59.381537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.0 1992
20.3%
0.999 1763
18.0%
345.0 674
 
6.9%
161.0 503
 
5.1%
6.0 474
 
4.8%
4.0 456
 
4.6%
97.6 429
 
4.4%
58.0 408
 
4.2%
40.0 401
 
4.1%
6993.0 400
 
4.1%
Other values (6) 2310
23.5%

Most occurring characters

ValueCountFrequency (%)
. 9810
16.7%
0 9782
16.6%
9 6897
11.7%
( 4905
8.3%
, 4905
8.3%
4905
8.3%
] 4905
8.3%
2 2752
 
4.7%
1 2563
 
4.4%
6 1806
 
3.1%
Other values (5) 5676
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58906
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 9810
16.7%
0 9782
16.6%
9 6897
11.7%
( 4905
8.3%
, 4905
8.3%
4905
8.3%
] 4905
8.3%
2 2752
 
4.7%
1 2563
 
4.4%
6 1806
 
3.1%
Other values (5) 5676
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58906
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 9810
16.7%
0 9782
16.6%
9 6897
11.7%
( 4905
8.3%
, 4905
8.3%
4905
8.3%
] 4905
8.3%
2 2752
 
4.7%
1 2563
 
4.4%
6 1806
 
3.1%
Other values (5) 5676
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58906
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 9810
16.7%
0 9782
16.6%
9 6897
11.7%
( 4905
8.3%
, 4905
8.3%
4905
8.3%
] 4905
8.3%
2 2752
 
4.7%
1 2563
 
4.4%
6 1806
 
3.1%
Other values (5) 5676
9.6%

Cluster_6
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size322.5 KiB
6
5004 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5004
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6
2nd row6
3rd row6
4th row6
5th row6

Common Values

ValueCountFrequency (%)
6 5004
100.0%

Length

2025-06-30T15:10:59.444032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-30T15:10:59.494523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
6 5004
100.0%

Most occurring characters

ValueCountFrequency (%)
6 5004
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 5004
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 5004
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 5004
100.0%

Interactions

2025-06-30T15:10:53.950180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:51.745823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:52.260940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:53.051105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:53.527758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:54.053667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:51.863699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:52.363193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:53.146892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:53.611200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:54.144164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:51.960882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:52.451407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:53.244555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:53.702030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:54.244492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:52.068474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:52.546432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:53.333705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:53.797203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:54.317254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:52.162618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:52.631151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:53.427476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-30T15:10:53.877648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-30T15:10:59.550782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_autenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_sociosdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
Estado1.0000.1820.1220.1290.0480.0380.0000.0000.0450.0920.1220.1450.1520.0331.0000.1680.057
TipoSocietario0.1821.0000.0930.1110.0260.1110.0160.0000.1070.1730.0600.2130.0840.1161.0000.3480.041
anio_preinscripcion0.1220.0931.0001.0000.0400.0160.0710.0000.0340.0970.1260.1090.0730.0470.9990.3270.046
antiguedad0.1290.1111.0001.0000.0450.0260.3080.0000.0400.0920.1580.1200.0880.109-0.9750.1100.179
cant_Apoderado0.0480.0260.0400.0451.0000.4060.1491.0000.7100.2000.1140.0810.1490.1611.0000.0000.294
cant_autenticado0.0380.1110.0160.0260.4061.0000.1200.6250.7760.1170.1280.0540.1910.1191.0000.0000.221
cant_procesos_adjudicado0.0000.0160.0710.3080.1490.1201.0000.0000.1860.0960.2620.1420.0840.566-0.2930.0000.379
cant_representante0.0000.0000.0000.0001.0000.6250.0001.0000.5670.2260.3010.0000.0000.0001.0000.0000.073
cant_sinMontoLimite0.0450.1070.0340.0400.7100.7760.1860.5671.0000.1000.1420.0890.2110.1341.0000.0000.277
cant_socios0.0920.1730.0970.0920.2000.1170.0960.2260.1001.0000.0530.0750.0960.1241.0000.1200.095
dcant_procesos_adjudicado0.1220.0600.1260.1580.1140.1280.2620.3010.1420.0531.0000.2230.1490.1070.0710.0610.119
dmonto_total_adjudicado0.1450.2130.1090.1200.0810.0540.1420.0000.0890.0750.2231.0000.0660.3510.1430.1030.074
dtotal_articulos_provee0.1520.0840.0730.0880.1490.1910.0840.0000.2110.0960.1490.0661.0000.0561.0000.0370.318
monto_total_adjudicado0.0330.1160.0470.1090.1610.1190.5660.0000.1340.1240.1070.3510.0561.000-0.1000.0000.184
periodo_preinscripcion1.0001.0000.999-0.9751.0001.000-0.2931.0001.0001.0000.0710.1431.000-0.1001.0000.896-0.185
provincia0.1680.3480.3270.1100.0000.0000.0000.0000.0000.1200.0610.1030.0370.0000.8961.0000.000
total_articulos_provee0.0570.0410.0460.1790.2940.2210.3790.0730.2770.0950.1190.0740.3180.184-0.1850.0001.000

Missing values

2025-06-30T15:10:54.495342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-30T15:10:54.694561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-30T15:10:54.930363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveeCluster_6
027236909900EMR VENTAS & SERVICIOS04/10/2016InscriptoPersona Física201610201668.01.069618e+075.0ChubutNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN68.0(9.424.898- 13.557.176](39.0, 1214.0](58.0, 97.6]6
130569211685Electricidad Chiclana de R. Santoianni y O.S. Rodriguez18/08/2016Desactualizado doc. vencidosSociedades De Hecho20160820167.04.975162e+065.0CABA1.0NaNNaNNaN1.0NaN1.0NaN1.0NaN330.0(4.727.330- 6.702.697](6.0, 8.0](161.0, 345.0]6
330708415487YLUM S.A.24/08/2016InscriptoSociedad Anónima2016082016111.05.959313e+075.0CABA1.0NaNNaNNaN1.0NaN1.0NaN1.0NaN83.0(46.718.747- 89.439.449](39.0, 1214.0](58.0, 97.6]6
630708538317CAROLS SA09/09/2016InscriptoSociedad Anónima201609201633.07.614297e+075.0CABA2.0NaNNaNNaN1.0NaN1.0NaN1.0NaN92.0(46.718.747- 89.439.449](19.0, 39.0](58.0, 97.6]6
920082883240SEGUMAX de HORACIO MIGUEL ESPOSITO18/10/2016Desactualizado doc. vencidosPersona Física20161020162.01.216063e+065.0CABANaNNaNNaNNaN1.0NaN1.0NaN1.0NaN263.0(890.758- 1.302.657](0.999, 2.0](161.0, 345.0]6
1130707882510SABADO URSI S.A.20/09/2016InscriptoSociedad Anónima201609201680.05.823704e+085.0CABA4.0NaNNaNNaN1.0NaN1.0NaN1.0NaN64.0(222.964.579- 46.172.150.151](39.0, 1214.0](58.0, 97.6]6
1320230506060sin datos15/08/2016InscriptoPersona Física201608201612.01.209260e+085.0CABANaNNaNNaNNaN1.0NaN1.0NaN1.0NaN5.0(89.439.449- 222.964.579](8.0, 12.0](4.0, 6.0]6
1620141208269sin datos23/09/2016InscriptoPersona Física20160920161.06.407980e+055.0CABANaNNaNNaNNaN1.0NaN1.0NaN1.0NaN16.0(599.760- 890.758](0.999, 2.0](15.0, 21.0]6
2030596555655COAMTRA S.A.16/11/2016Desactualizado doc. vencidosSociedad Anónima20161120161.05.669606e+055.0Buenos Aires1.0NaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(377.939- 599.760](0.999, 2.0](0.999, 2.0]6
2130686199017MEKANO SRL16/11/2016InscriptoS.R.L20161120165.01.152604e+075.0CABA2.0NaNNaNNaN1.0NaN1.0NaN1.0NaN11.0(9.424.898- 13.557.176](4.0, 5.0](8.0, 11.0]6
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveeCluster_6
1005820385356243DIAZ JONATAN MACIEL13/09/2022InscriptoPersona Física20220920221.05.666667e+050.0ChubutNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(377.939- 599.760](0.999, 2.0](0.999, 2.0]6
1005920322310944E.F.R.26/08/2022InscriptoPersona Física20220820221.04.047619e+050.0JujuyNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN253.0(377.939- 599.760](0.999, 2.0](161.0, 345.0]6
1006324925253304ALBERTO FREDY MARTIN15/02/2021InscriptoPersona Física20210220211.01.398857e+060.0Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN29.0(1.302.657- 1.793.326](0.999, 2.0](21.0, 29.0]6
1006520328156742M&M01/09/2022InscriptoPersona Física20220920221.00.000000e+000.0CorrientesNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(-0- 33.011](0.999, 2.0](0.999, 2.0]6
1006720171591563BIOTECNIKA16/06/2022InscriptoPersona Física20220620221.02.986090e+060.0Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN22.0(2.483.085- 3.396.600](0.999, 2.0](21.0, 29.0]6
1006820293290416ELIAS MARTIN SEGURA26/08/2022InscriptoPersona Física20220820221.01.873469e+050.0CABANaNNaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(104.767- 224.078](0.999, 2.0](0.999, 2.0]6
1006920240423759FEDERICO MARTIN NUÑEZ26/08/2022InscriptoPersona Física20220820221.03.226531e+050.0CABANaNNaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(224.078- 377.939](0.999, 2.0](0.999, 2.0]6
1007120287286687LAZARTE MARIO03/08/2022InscriptoPersona Física20220820221.05.792102e+060.0Buenos AiresNaNNaNNaNNaN1.0NaN1.0NaN1.0NaN1.0(4.727.330- 6.702.697](0.999, 2.0](0.999, 2.0]6
1007430716441098LISTOS PARA RODAR SAS16/08/2019InscriptoOtras Formas Societarias20190820191.01.196939e+052.0CABA2.0NaNNaNNaN1.0NaN1.0NaN1.0NaN19.0(104.767- 224.078](0.999, 2.0](15.0, 21.0]6
1007527331126530sin datos18/08/2022InscriptoPersona Física20220820221.09.367347e+040.0CABANaNNaNNaNNaN1.0NaN1.0NaN1.0NaN4.0(33.011- 104.767](0.999, 2.0](3.0, 4.0]6